Bluish Veil Detection and Lesion Classification using Custom Deep Learnable Layers with Explainable Artificial Intelligence (XAI)
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
Melanoma, one of the deadliest types of skin cancer, accounts for thousands
of fatalities globally. The bluish, blue-whitish, or blue-white veil (BWV) is a
critical feature for diagnosing melanoma, yet research into detecting BWV in
dermatological images is limited. This study utilizes a non-annotated skin
lesion dataset, which is converted into an annotated dataset using a proposed
imaging algorithm based on color threshold techniques on lesion patches and
color palettes. A Deep Convolutional Neural Network (DCNN) is designed and
trained separately on three individual and combined dermoscopic datasets, using
custom layers instead of standard activation function layers. The model is
developed to categorize skin lesions based on the presence of BWV. The proposed
DCNN demonstrates superior performance compared to conventional BWV detection
models across different datasets. The model achieves a testing accuracy of
85.71% on the augmented PH2 dataset, 95.00% on the augmented ISIC archive
dataset, 95.05% on the combined augmented (PH2+ISIC archive) dataset, and
90.00% on the Derm7pt dataset. An explainable artificial intelligence (XAI)
algorithm is subsequently applied to interpret the DCNN's decision-making
process regarding BWV detection. The proposed approach, coupled with XAI,
significantly improves the detection of BWV in skin lesions, outperforming
existing models and providing a robust tool for early melanoma diagnosis.
